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---
base_model: openai/whisper-small
datasets:
- mozilla-foundation/common_voice_11_0
language:
- yo
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Small Yo - Bola Ologundudu
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: yo
split: None
args: 'config: yo, split: test'
metrics:
- type: wer
value: 70.61345018098686
name: Wer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Yoruba - Bola Ologundudu
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2225
- Wer: 70.6135
## Model description
>>> from transformers import pipeline
>>> import torch
>>> modelName="ajibs75/whisper-small-yoruba"
>>> device = 0 if torch.cuda.is_available() else "cpu"
>>> pipe = pipeline(task="automatic-speech-recognition",model=modelName,chunk_length_s=30,device=device,)
>>> pipe.model.config.forced_decoder_ids = pipe.tokenizer.get_decoder_prompt_ids(language="yo", task="transcribe")
>>> audio = "sample.mp3"
>>> text = pipe(audio)
>>> transacribed_audio = text["text"]
>>> print(transacribed_audio)
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.066 | 7.6923 | 1000 | 0.8962 | 74.0141 |
| 0.004 | 15.3846 | 2000 | 1.1411 | 71.6613 |
| 0.0004 | 23.0769 | 3000 | 1.1959 | 70.6516 |
| 0.0003 | 30.7692 | 4000 | 1.2225 | 70.6135 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.1.0+cu118
- Datasets 2.20.0
- Tokenizers 0.19.1
|